3D printing is quite popular nowadays, which fabricates a 3D object by superimposing 2D patterns layer by layer.
Since the process for fabricating a 3D object is time-consuming and has low precision and repeatability nowadays, a measuring system is required to measure topography of each layer in two dimensions and variation of height in a third dimension so as to ensure the quality of the 3D object.
However, due to an insufficient resolution of a current available measuring system, it is difficult to monitor the topography and height variation of the 3D object and measure flatness of spread powder on a measuring surface (2D) of the 3D object, which adversely affects the quality and yield of the 3D object.
Further, in order to fabricate a 3D object having N layers, a current available measuring system generally performs 2N operations to capture images of N measuring surfaces and N reference surfaces, thereby obtaining the height variation of each layer of the object.
Furthermore, an image defect algorithm such as singular value decomposition (SVD) or Hilbert-Huang transform (HHT) is usually used to analyze the flatness of spread powder on the 3D object, which requires a large number of data operations to obtain background information and defective images.